Results for “multi objective optimization” — Page 35

DOE OSTI2022

System and method for management of electrochemical energy storage devices

Methods and software for electrochemical energy storage management including operation, planning, and valuation. The decision objective for operation is to maximize the total/remaining life-cycle benefit of electrochemical energy storage, subject to degradation and other operational constraints. The operational decisions are both short term and long term, to address the intertemporal trade-offs brought by degradation issues. Two metrics are proposed for operational and planning decisions respectively: marginal benefit of usage and average benefit of usage. A sample algorithm is provided to solve the multi-timescale optimization problem.

Whitacre, Jay↗

NASA NTRS1991

Multi-parameter optimization tool for low-cost commercial fuselage crown designs

The work in progress for developing a methodology and software tool to aid in the optimal design of composite structures is discussed. The methodology is being developed to take advantage of the ability to tailor the composite material in conjunction with the design of the structure. The composites optimization design software UWCODA was found to be very successful in preliminary testing and early experience. UWCODA is a composites design code that uses a number of plies and fiber angles as design variables, employs maximum strain failure criteria for objective function and additional constraints, includes Boeing design tools for stiffened panels, and includes stiffener geometry in the design variables.

Zabinsky, Zelda↗

DOE OSTI2025

Analog Signal Multiplexing System for the IOTA Proton Injector

he Fermilab Accelerator Science and Technology (FAST) Facility at FNAL is a dedicated research and development center focused on advancing particle accelerator technologies for future applications worldwide. Currently, a key objective of FAST Opera-tions is to commission the 2.5 MeV IOTA Proton Injec-tor (IPI) and enable proton injection into the Integrable Optics Test Accelerator (IOTA) storage ring. The low and medium-energy sections of the IPI include four frame-style dipole trims and two multi-function cor-rectors with independently controlled coils, requiring readout of 32 analog channels for current and voltage monitoring in total. To reduce cost and optimize rack space within the PLC-based control system, a 32-to-4 analog signal multiplexing system was designed and implemented. This system enables real-time readback of excitation parameters from all magnetic correctors. This paper presents the design, construction, implementation, and performance of the multiplexing system.

MacLean, Daniel R. [Fermilab] (ORCID:0000000210103↗

DOE OSTI2023

A comparison of eight optimization methods applied to a wind farm layout optimization problem

Abstract. Selecting a wind farm layout optimization method is difficult. Comparisons between optimization methods in different papers can be uncertain due to the difficulty of exactly reproducing the objective function. Comparisons by just a few authors in one paper can be uncertain if the authors do not have experience using each algorithm. In this work we provide an algorithm comparison for a wind farm layout optimization case study between eight optimization methods applied, or directed, by researchers who developed those algorithms or who had other experience using them. We provided the objective function to each researcher to avoid ambiguity about relative performance due to a difference in objective function. While these comparisons are not perfect, we try to treat each algorithm more fairly by having researchers with experience using each algorithm apply each algorithm and by having a common objective function provided for analysis. The case study is from the International Energy Association (IEA) Wind Task 37, based on the Borssele III and IV wind farms with 81 turbines. Of particular interest in this case study is the presence of disconnected boundary regions and concave boundary features. The optimization methods studied represent a wide range of approaches, including gradient-free, gradient-based, and hybrid methods; discrete and continuous problem formulations; single-run and multi-start approaches; and mathematical and heuristic algorithms. We provide descriptions and references (where applicable) for each optimization method, as well as lists of pros and cons, to help readers determine an appropriate method for their use case. All the optimization methods perform similarly, with optimized wake loss values between 15.48 % and 15.70 % as compared to 17.28 % for the unoptimized provided layout. Each of the layouts found were different, but all layouts exhibited similar characteristics. Strong similarities across all the layouts include tightly packing wind turbines along the outer borders, loosely spacing turbines in the internal regions, and allocating similar numbers of turbines to each discrete boundary region. The best layout by annual energy production (AEP) was found using a new sequential allocation method, discrete exploration-based optimization (DEBO). Based on the results in this study, it appears that using an optimization algorithm can significantly improve wind farm performance, but there are many optimization methods that can perform well on the wind farm layout optimization problem, given that they are applied correctly.

17 WIND ENERGY↗

DOE OSTI2021

Explainable Neural Architecture Search (XNAS)

Code for the paper Learning Interpretable Models Through Multi-Objective Neural Architecture Search by Zachariah Carmichael, Tim Moon, and Sam Ade Jacobs. Monumental advances in deep learning have led to unprecedented achievements across a multitude of domains. While the performance of deep neural networks is indubitable, the architectural design and interpretability of such models are nontrivial. Research has been introduced to automate the design of neural network architectures through neural architecture search (NAS). Recent progress has made these methods more pragmatic by exploiting distributed computation and novel optimization algorithms. However, there is little work in optimizing architectures for interpretability. To this end, we propose a multiobjective distributed NAS framework that optimizes for both task performance and introspection. We leverage the non-dominated sorting genetic algorithm (NSGA-II) and explainable AI (XAI) techniques to reward architectures that can be better comprehended by humans. The framework is evaluated on several image classification datasets. We demonstrate that jointly optimizing for introspection ability and task error leads to more disentangled architectures that perform within tolerable error.

Carmichael, ZachariahJ↗

NASA NTRS1996

Decision-Aiding and Optimization for Vertical Navigation of Long-Haul Aircraft

Most decisions made in the cockpit are related to safety, and have therefore been proceduralized in order to reduce risk. There are very few which are made on the basis of a value metric such as economic cost. One which can be shown to be value based, however, is the selection of a flight profile. Fuel consumption and flight time both have a substantial effect on aircraft operating cost, but they cannot be minimized simultaneously. In addition, winds, turbulence, and performance vary widely with altitude and time. These factors make it important and difficult for pilots to (a) evaluate the outcomes associated with a particular trajectory before it is flown and (b) decide among possible trajectories. The two elements of this problem considered here are: (1) determining what constitutes optimality, and (2) finding optimal trajectories. Pilots and dispatchers from major u.s. airlines were surveyed to determine which attributes of the outcome of a flight they considered the most important. Avoiding turbulence-for passenger comfort-topped the list of items which were not safety related. Pilots' decision making about the selection of flight profile on the basis of flight time, fuel burn, and exposure to turbulence was then observed. Of the several behavioral and prescriptive decision models invoked to explain the pilots' choices, utility maximization is shown to best reproduce the pilots' decisions. After considering more traditional methods for optimizing trajectories, a novel method is developed using a genetic algorithm (GA) operating on a discrete representation of the trajectory search space. The representation is a sequence of command altitudes, and was chosen to be compatible with the constraints imposed by Air Traffic Control, and with the training given to pilots. Since trajectory evaluation for the GA is performed holistically, a wide class of objective functions can be optimized easily. Also, using the GA it is possible to compare the costs associated with different airspace design and air traffic management policies. A decision aid is proposed which would combine the pilot's notion of optimality with the GA-based optimization, provide the pilot with a number of alternative pareto-optimal trajectories, and allow him to consider unmodelled attributes and constraints in choosing among them. A solution to the problem of displaying alternatives in a multi-attribute decision space is also presented.

Patrick, Nicholas J. M.↗

DOE OSTI2025

Evaluation of normalization strategies for mass spectrometry-based multi-omics datasets

Introduction Data normalization is crucial for multi-omics integration, reducing systematic errors and maximizing the likelihood of discovering true biological variation. Most studies assess normalization for a single omics type or use datasets from separate experiments. Few address time-course data, where normalization might bias temporal differentiation. In this study, we compared common normalization methods and a machine learning approach, Systematical Error Removal using Random Forest (SERRF), using multi-omics datasets generated from the same experiment—even from the same cell lysate. Objectives To develop a straightforward process to assess normalization effects and identify the most robust methods across multi-omics datasets. Methods We analyzed metabolomics, lipidomics, and proteomics datasets from primary human cardiomyocytes and motor neurons exposed to acetylcholine-active compounds over time. Normalization effectiveness was evaluated based on improvement in QC features consistency and observing the change in treatment and time-related variance. Results Probabilistic Quotient Normalization (PQN) and Locally Estimated Scatterplot Smoothing (LOESS) QC were identified as optimal for metabolomics and lipidomics, while PQN, Median, and LOESS normalization excelled for proteomics. These methods consistently enhanced QC feature consistency in metabolomics and lipidomics, and preserved time-related variance or treatment-related variance in proteomics, demonstrating their effectiveness and robustness. SERRF normalization, applied only to metabolomics in this study, outperformed other methods in some datasets but inadvertently masked treatment-related variance in others. Conclusion Our evaluation identified PQN and LoessQC as the top methods for metabolomics and lipidomics, and PQN, Median, and Loess normalization for proteomics, in multi-omics integration in a temporal study.

60 APPLIED LIFE SCIENCES↗

NASA NTRS2001

Optimizing Orbit-Instrument Configuration for Global Precipitation Mission (GPM) Satellite Fleet

Following the scientific success of the Tropical Rainfall Measuring Mission (TRMM) spearheaded by a group of NASA and NASDA scientists, their external scientific collaborators, and additional investigators within the European Union's TRMM Research Program (EUROTRMM), there has been substantial progress towards the development of a new internationally organized, global scale, and satellite-based precipitation measuring mission. The highlights of this newly developing mission are a greatly expanded scope of measuring capability and a more diversified set of science objectives. The mission is called the Global Precipitation Mission (GPM). Notionally, GPM will be a constellation-type mission involving a fleet of nine satellites. In this fleet, one member is referred to as the "core" spacecraft flown in an approximately 70 degree inclined non-sun-synchronous orbit, somewhat similar to TRMM in that it carries both a multi-channel polarized passive microwave radiometer (PMW) and a radar system, but in this case it will be a dual frequency Ku-Ka band radar system enabling explicit measurements of microphysical DSD properties. The remainder of fleet members are eight orbit-synchronized, sun-synchronous "constellation" spacecraft each carrying some type of multi-channel PMW radiometer, enabling no worse than 3-hour diurnal sampling over the entire globe. In this configuration the "core" spacecraft serves as a high quality reference platform for training and calibrating the PMW rain retrieval algorithms used with the "constellation" radiometers. Within NASA, GPM has advanced to the pre-formulation phase which has enabled the initiation of a set of science and technology studies which will help lead to the final mission design some time in the 2003 period. This presentation first provides an overview of the notional GPM program and mission design, including its organizational and programmatic concepts, scientific agenda, expected instrument package, and basic flight architecture. Following this introduction, we focus specifically on the last topic, that being an analysis which leads to an optimal flight architecture dictated in part by science requirements but constrained by allowable orbital mechanics, instrument scan patterns, and antenna aperture properties. Because the optimal architecture involves an interplay between orbit mechanics and instrument specifications, it is important to recognize that in attempting to serve various scientific themes, the final optimal architecture will represent a compromise concerning dynamic range, spatial resolution, sampling interval, pointing, beam coincidence, and measurement uncertainty. Moreover, cost becomes a major factor in seeking the optimal architecture through the pathways of antenna and instrument scan designs, as well as propulsion requirements associated with the orbit heights of various "constellation" members. Although the results presented at the IGARSS-2001 meeting will likely not be the fully refined flight architecture specifications, they are expected to be nearly complete.

Smith, Eric A.↗

NASA NTRS2011

Multi-Parameter Aerosol Scattering Sensor

This work relates to the development of sensors that measure specific aerosol properties. These properties are in the form of integrated moment distributions, i.e., total surface area, total mass, etc., or mathematical combinations of these moment distributions. Specifically, the innovation involves two fundamental features: a computational tool to design and optimize such sensors and the embodiment of these sensors in actual practice. The measurement of aerosol properties is a problem of general interest. Applications include, but are not limited to, environmental monitoring, assessment of human respiratory health, fire detection, emission characterization and control, and pollutant monitoring. The objectives for sensor development include increased accuracy and/or dynamic range, the inclusion in a single sensor of the ability to measure multiple aerosol properties, and developing an overall physical package that is rugged, compact, and low in power consumption, so as to enable deployment in harsh or confined field applications, and as distributed sensor networks. Existing instruments for this purpose include scattering photometers, direct-reading mass instruments, Beta absorption devices, differential mobility analyzers, and gravitational samplers. The family of sensors reported here is predicated on the interaction of light and matter; specifically, the scattering of light from distributions of aerosol particles. The particular arrangement of the sensor, e.g. the wavelength(s) of incident radiation, the number and location of optical detectors, etc., can be derived so as to optimize the sensor response to aerosol properties of practical interest. A key feature of the design is the potential embodiment as an extremely compact, integrated microsensor package. This is of fundamental importance, as it enables numerous previously inaccessible applications. The embodiment of these sensors is inherently low maintenance and high reliability by design. The novel and unique features include the underlying computational underpinning that allows the optimization for specific applications, and the physical embodiment that affords the construction of a compact, durable, and reliable integrated package. The advantage appears in the form of increased accuracy relative to existing instruments, and the applications enabled by the physical attributes of the resulting configuration

Greenberg, Paul S.↗

DOE OSTI2020

Robust Group Subspace Recovery: A New Approach for Multi-Modality Data Fusion

Robust Subspace Recovery (RoSuRe) algorithm was recently introduced as a principled and numerically efficient algorithm that unfolds underlying Unions of Subspaces (UoS) structure, present in the data. The union of Subspaces (UoS) is capable of identifying more complex trends in data sets than simple linear models. In this work, we build on and extend RoSuRe to prospect the structure of different data modalities individually. We propose a novel multi-modal data fusion approach based on group sparsity which we refer to as Robust Group Subspace Recovery (RoGSuRe). Relying on a bi-sparsity pursuit paradigm and non-smooth optimization techniques, the introduced framework learns a new joint representation of the time series from different data modalities, respecting an underlying UoS model. We subsequently integrate the obtained structures to form a unified subspace structure. The proposed approach exploits the structural dependencies between the different modalities data to cluster the associated target objects. The resulting fusion of the unlabeled sensors’ data from experiments on audio and magnetic data has shown that our method is competitive with other state of the art subspace clustering methods. The resulting UoS structure is employed to classify newly observed data points, highlighting the abstraction capacity of the proposed method.

47 OTHER INSTRUMENTATION↗

NASA NTRS1989

Multiplexing electro-optic architectures for advanced aircraft integrated flight control systems

This report describes the results of a 10 month program sponsored by NASA. The objective of this program was to evaluate various optical sensor modulation technologies and to design an optimal Electro-Optic Architecture (EOA) for servicing remote clusters of sensors and actuators in advanced aircraft flight control systems. The EOA's supply optical power to remote sensors and actuators, process the modulated optical signals returned from the sensors, and produce conditioned electrical signals acceptable for use by a digital flight control computer or Vehicle Management System (VMS) computer. This study was part of a multi-year initiative under the Fiber Optic Control System Integration (FOCSI) program to design, develop, and test a totally integrated fiber optic flight/propulsion control system for application to advanced aircraft. Unlike earlier FOCSI studies, this program concentrated on the design of the EOA interface rather than the optical transducer technology itself.

Seal, D. W.↗

NASA NTRS1996

Decision-Aiding and Optimization for Vertical Navigation of Long-Haul Aircraft

Most decisions made in the cockpit are related to safety, and have therefore been proceduralized in order to reduce risk. There are very few which are made on the basis of a value metric such as economic cost. One which can be shown to be value based, however, is the selection of a flight profile. Fuel consumption and flight time both have a substantial effect on aircraft operating cost, but they cannot be minimized simultaneously. In addition, winds, turbulence, and performance x,ary widely with altitude and time. These factors make it important and difficult for pilots to (a) evaluate the outcomes associated with a particular trajectory before it is flown and (b) decide among possible trajectories. The two elements of this problem considered here are (1) determining, what constitutes optimality, and (2) finding optimal trajectories. Pilots and dispatchers from major U.S. airlines were surveyed to determine which attributes of the outcome of a flight they considered the most important. Avoiding turbulence-for passenger comfort topped the list of items which were not safety related. Pilots' decision making about the selection of flight profile on the basis of flight time, fuel burn, and exposure to turbulence was then observed. Of the several behavioral and prescriptive decision models invoked to explain the pilots' choices, utility maximization is shown to best reproduce the pilots' decisions. After considering more traditional methods for optimizing trajectories, a novel method is developed using a genetic algorithm (GA) operating on a discrete representation of the trajectory search space. The representation is a sequence of command altitudes, and was chosen to be compatible with the constraints imposed by Air Traffic Control, and with the training given to pilots. Since trajectory evaluation for the GA is performed holistically, a wide class of objective functions can be optimized easily. Also, using the GA it is possible to compare the costs associated with different airspace design and air traffic management policies. A decision aid is proposed which would combine the pilot's notion of optimility with the GA-based optimization, provide the pilot with a number of alternative pareto-optimal trajectories, and allow him to consider un-modelled attributes and constraints in choosing among them. A solution to the problem of displaying alternatives in a multi-attribute decision space is also presented.

Patrick, Nicholas J. M.↗

DOE OSTI2023

Advanced Structured Adsorbent Architectures for Transformative Carbon Dioxide Capture Performance (Final Report)

Svante is a world leader at using solid sorbents for low-cost Carbon Dioxide (CO 2 ) capture, a technology which is recognized as critical in meeting the dual mandates of energy security/reliability and the mitigation of man-made CO 2 emissions. Svante has been developing proprietary adsorbent material compositions, forming them into structured laminates, developing and optimizing process cycles, and system design for efficient capture of CO 2 from post-combustion flue gases of thermal power plants and industrial facilities. The deployment of first-generation CO 2 capture technology has been significantly hampered by high costs and energy penalties, among other barriers. Second generation CO 2 capture technologies (including the Mark I variant of Svante’s Veloxotherm™ adsorption-based technology), utilizing single adsorbent architecture, show promise for reducing the barriers to deploying CO 2 capture plants in commercially meaningful numbers. The objective of this project was to evaluate the Recipient’s transformational (Mark-II) VeloxoTherm™ Technology via the development and bench-scale testing of an advanced structured adsorbent, including novel Bi-layer, laminated adsorbent structures and segmented beds. Svante selected, synthesized, and characterized tailored solid adsorbents for computational modeling, advanced structured adsorbent development, process simulations, and dynamic bench scale (~1-10 kg/day CO 2 captured) testing using an existing single-bed VeloxoTherm™ Station (VTS) coupled with a natural gas-fired boiler. Segmented beds used the in-house, multi-bed Process Demonstration Unit (PDU) to demonstrate key performance indicators (KPIs), such as recovery, product purity, regeneration energy, and the integrated system's productivity in lifetime analysis. Segmented beds were used at a 1 tonne per day (TPD) unit at an industrial site to provide bench-scale validation of performance in an industrial setting. Svante was developing and optimizing the post-combustion CO 2 adsorption technology architectures, including the Bi-layer and segmented laminated adsorbent structure design, integrated rapid cycle temperature swing adsorption (RC-TSA) cycle, flow path architecture, and adsorbent bed construction and packaging (including gas porting) to progress towards achievement of DOE’s Transformational CO 2 Capture goals of 95% CO 2 purity and a cost of electricity at least 30% lower than a supercritical Pulverized Coal (PC) power plant with CO 2 capture, or approximately $30 per tonne of CO 2 captured ready for demonstration by 2030. The main requirements to reach the DOE target cost of carbon capture below $30/MT using Rapid-Cycling Temperature Swing Adsorption (RC-TSA) are as follows: (1) Increased capacity at different CO 2 concentrations, (2) Increased sorbents cycle life, (3) Increased O 2 resistance, and (4) Decreased steam requirement to extract 1 kg of CO 2 .

20 FOSSIL-FUELED POWER PLANTS↗

DOE OSTI2020

Development of Miniaturized High Temperature Multi Process Monitoring System

The main objective of this research program is to design, manufacture, and demonstrate a miniaturized, multi-process, monitoring system (mMPMS) for boiler condition management and easy system deployment to obtain a higher spatial resolution. This system will facilitate a Condition-Based Maintenance (CBM) philosophy that actively monitors the health of assets to predict and prevent failures and maximize availability and generating capacity at a reduced cost. CBM systems can provide boiler data that the advanced process control (APC) system can utilize for plant performance optimization, which is increasingly relevant as coal power plants shift from predominantly base-load operation to predominantly transient operation involving large load swings. The mMPMS is based on an electrochemical sensor that provides a real-time indication of the risk of damage to key locations in the radiant or convective section of a coal-fired boiler such as metal loss rates, heat flux, metal surface temperature, and deposit thickness. These indications can be utilized to optimize boiler performance as well as improve boiler availability in conjunction with corresponding operating conditions. This monitoring system was developed and tested in the high-temperature regions of coal-fired utility boilers in this project but can be applied to many other industries and applications as well. This project leveraged the existing electrochemical noise-based monitoring system and the new sensor design is small enough to be installed through the webbing of the waterwalls without the need for long shut-downs to bend tubes and to make it feasible to obtain high spatial resolution in the boiler. Data is transferred to the plant distributed control system (DCS) and any other control system. The sensor body that houses the sensor assembly was designed to ensure good conductive contact with boiler tubes to ensure the sensor is held at an identical temperature to the tube surface temperature. The data acquisition and signal conditioning modules were redesigned into a small footprint with optimized cooling of the module. System software was developed specifically for the new signal conditioning module and is compatible with plant PLCs. After the preliminary testing at a pilot-scale facility, the three mMPMS were installed at a full-scale pulverized coal-fired plant, PacifiCorp’s Hunter 3. The systems were demonstrated over 20,000 hours at the plant during regular operation. Also, the sensor data was fed to the plant’s advanced process control system to evaluate the corrosion control by the operation changes and utilized to understand the impacts of load cycling with different ramping up and down speeds. At the end of the project, the systems were converted to the permanent installation at the power plant to be used with the advanced process control system installed at the plant.

Advanced Sensors, Corrosion, Ash Deposition, Optim↗

DOE OSTI2021

A. Development of Miniaturized High Temperature Multi Process Monitoring System

The main objective of this research program is to design, manufacture, and demonstrate a miniaturized, multi-process, monitoring system (mMPMS) for boiler condition management and easy system deployment to obtain a higher spatial resolution. This system will facilitate a Condition-Based Maintenance (CBM) philosophy that actively monitors the health of assets to predict and prevent failures and maximize availability and generating capacity at a reduced cost. CBM systems can provide boiler data that the advanced process control (APC) system can utilize for plant performance optimization, which is increasingly relevant as coal power plants shift from predominantly base-load operation to predominantly transient operation involving large load swings. The mMPMS is based on an electrochemical sensor that provides a real-time indication of the risk of damage to key locations in the radiant or convective section of a coal-fired boiler such as metal loss rates, heat flux, metal surface temperature, and deposit thickness. These indications can be utilized to optimize boiler performance as well as improve boiler availability in conjunction with corresponding operating conditions. This monitoring system was developed and tested in the high-temperature regions of coal-fired utility boilers in this project but can be applied to many other industries and applications as well. This project leveraged the existing electrochemical noise-based monitoring system and the new sensor design is small enough to be installed through the webbing of the waterwalls without the need for long shut-downs to bend tubes and to make it feasible to obtain high spatial resolution in the boiler. Data is transferred to the plant distributed control system (DCS) and any other control system. The sensor body that houses the sensor assembly was designed to ensure good conductive contact with boiler tubes to ensure the sensor is held at an identical temperature to the tube surface temperature. The data acquisition and signal conditioning modules were redesigned into a small footprint with optimized cooling of the module. System software was developed specifically for the new signal conditioning module and is compatible with plant PLCs. After the preliminary testing at a pilot-scale facility, the three mMPMS were installed at a full-scale pulverized coal-fired plant, PacifiCorp’s Hunter 3. The systems were demonstrated over 20,000 hours at the plant during regular operation. Also, the sensor data was fed to the plant’s advanced process control system to evaluate the corrosion control by the operation changes and utilized to understand the impacts of load cycling with different ramping up and down speeds. At the end of the project, the systems were converted to the permanent installation at the power plant to be used with the advanced process control system installed at the plant.

Advanced Sensors, Corrosion, Ash Deposition, Optim↗

DOE OSTI2021

High-Temperature Multi-Process Sensor Development for a PC-Fired Unit

The main objective of this research program is to design, manufacture, and demonstrate a miniaturized, multi-process, monitoring system (mMPMS) for boiler condition management and easy system deployment to obtain a higher spatial resolution. This system will facilitate a Condition-Based Maintenance (CBM) philosophy that actively monitors the health of assets to predict and prevent failures and maximize availability and generating capacity at a reduced cost. CBM systems can provide boiler data that the advanced process control (APC) system can utilize for plant performance optimization, which is increasingly relevant as coal power plants shift from predominantly base-load operation to predominantly transient operation involving large load swings. The mMPMS is based on an electrochemical sensor that provides a real-time indication of the risk of damage to key locations in the radiant or convective section of a coal-fired boiler such as metal loss rates, heat flux, metal surface temperature, and deposit thickness. These indications can be utilized to optimize boiler performance as well as improve boiler availability in conjunction with corresponding operating conditions. This monitoring system was developed and tested in the high-temperature regions of coal-fired utility boilers in this project but can be applied to many other industries and applications as well. This project leveraged the existing electrochemical noise-based monitoring system and the new sensor design is small enough to be installed through the webbing of the waterwalls without the need for long shut-downs to bend tubes and to make it feasible to obtain high spatial resolution in the boiler. Data is transferred to the plant distributed control system (DCS) and any other control system. The sensor body that houses the sensor assembly was designed to ensure good conductive contact with boiler tubes to ensure the sensor is held at an identical temperature to the tube surface temperature. The data acquisition and signal conditioning modules were redesigned into a small footprint with optimized cooling of the module. System software was developed specifically for the new signal conditioning module and is compatible with plant PLCs. After the preliminary testing at a pilot-scale facility, the three mMPMS were installed at a full-scale pulverized coal-fired plant, PacifiCorp’s Hunter 3. The systems were demonstrated over 20,000 hours at the plant during regular operation. Also, the sensor data was fed to the plant’s advanced process control system to evaluate the corrosion control by the operation changes and utilized to understand the impacts of load cycling with different ramping up and down speeds. At the end of the project, the systems were converted to the permanent installation at the power plant to be used with the advanced process control system installed at the plant.

Advanced Sensors, Corrosion, Ash Deposition, Optim↗

DOE OSTI2022

Real-time sensor measurements to evaluate impacts of load cycling on high-temperature fire-side corrosion in a PC boiler

The main objective of this research program is to design, manufacture, and demonstrate a miniaturized, multi-process, monitoring system (mMPMS) for boiler condition management and easy system deployment to obtain a higher spatial resolution. This system will facilitate a Condition-Based Maintenance (CBM) philosophy that actively monitors the health of assets to predict and prevent failures and maximize availability and generating capacity at a reduced cost. CBM systems can provide boiler data that the advanced process control (APC) system can utilize for plant performance optimization, which is increasingly relevant as coal power plants shift from predominantly base-load operation to predominantly transient operation involving large load swings. The mMPMS is based on an electrochemical sensor that provides a real-time indication of the risk of damage to key locations in the radiant or convective section of a coal-fired boiler such as metal loss rates, heat flux, metal surface temperature, and deposit thickness. These indications can be utilized to optimize boiler performance as well as improve boiler availability in conjunction with corresponding operating conditions. This monitoring system was developed and tested in the high-temperature regions of coal-fired utility boilers in this project but can be applied to many other industries and applications as well. This project leveraged the existing electrochemical noise-based monitoring system and the new sensor design is small enough to be installed through the webbing of the waterwalls without the need for long shut-downs to bend tubes and to make it feasible to obtain high spatial resolution in the boiler. Data is transferred to the plant distributed control system (DCS) and any other control system. The sensor body that houses the sensor assembly was designed to ensure good conductive contact with boiler tubes to ensure the sensor is held at an identical temperature to the tube surface temperature. The data acquisition and signal conditioning modules were redesigned into a small footprint with optimized cooling of the module. System software was developed specifically for the new signal conditioning module and is compatible with plant PLCs. After the preliminary testing at a pilot-scale facility, the three mMPMS were installed at a full-scale pulverized coal-fired plant, PacifiCorp’s Hunter 3. The systems were demonstrated over 20,000 hours at the plant during regular operation. Also, the sensor data was fed to the plant’s advanced process control system to evaluate the corrosion control by the operation changes and utilized to understand the impacts of load cycling with different ramping up and down speeds. At the end of the project, the systems were converted to the permanent installation at the power plant to be used with the advanced process control system installed at the plant.

Advanced Sensors, Corrosion, Ash Deposition, Optim↗

DOE OSTI2021

High-Temperature Multi-Process Sensor Development and Demonstration at Coalfired Power Plants

The main objective of this research program is to design, manufacture, and demonstrate a miniaturized, multi-process, monitoring system (mMPMS) for boiler condition management and easy system deployment to obtain a higher spatial resolution. This system will facilitate a Condition-Based Maintenance (CBM) philosophy that actively monitors the health of assets to predict and prevent failures and maximize availability and generating capacity at a reduced cost. CBM systems can provide boiler data that the advanced process control (APC) system can utilize for plant performance optimization, which is increasingly relevant as coal power plants shift from predominantly base-load operation to predominantly transient operation involving large load swings. The mMPMS is based on an electrochemical sensor that provides a real-time indication of the risk of damage to key locations in the radiant or convective section of a coal-fired boiler such as metal loss rates, heat flux, metal surface temperature, and deposit thickness. These indications can be utilized to optimize boiler performance as well as improve boiler availability in conjunction with corresponding operating conditions. This monitoring system was developed and tested in the high-temperature regions of coal-fired utility boilers in this project but can be applied to many other industries and applications as well. This project leveraged the existing electrochemical noise-based monitoring system and the new sensor design is small enough to be installed through the webbing of the waterwalls without the need for long shut-downs to bend tubes and to make it feasible to obtain high spatial resolution in the boiler. Data is transferred to the plant distributed control system (DCS) and any other control system. The sensor body that houses the sensor assembly was designed to ensure good conductive contact with boiler tubes to ensure the sensor is held at an identical temperature to the tube surface temperature. The data acquisition and signal conditioning modules were redesigned into a small footprint with optimized cooling of the module. System software was developed specifically for the new signal conditioning module and is compatible with plant PLCs. After the preliminary testing at a pilot-scale facility, the three mMPMS were installed at a full-scale pulverized coal-fired plant, PacifiCorp’s Hunter 3. The systems were demonstrated over 20,000 hours at the plant during regular operation. Also, the sensor data was fed to the plant’s advanced process control system to evaluate the corrosion control by the operation changes and utilized to understand the impacts of load cycling with different ramping up and down speeds. At the end of the project, the systems were converted to the permanent installation at the power plant to be used with the advanced process control system installed at the plant.

Advanced Sensors, Corrosion, Ash Deposition, Optim↗